Author: Fritz

Object detection is a technology related to computer vision and image processing that deals with detecting and locating instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.

In this post, we’ll briefly discuss feature descriptors, and specifically Histogram of Oriented Gradients (HOG). We’ll also provide an overview of deep learning approaches to about object detection, including Region-based Convolutional Neural Networks (RCNN) and YOLO(you only look once).

Continue reading Introduction to basic object detection algorithms

Are you looking for a computationally cheap, easy-to-explain linear estimator that’s based on simple mathematics? Look no further than OLS!

OLS stands for ordinary least squares. OLS is heavily used in econometrics—a branch of economics where statistical methods are used to find the insights in economic data.

As we know, the simplest linear regression algorithm assumes that the relationship between an independent variable (x) and dependent variable (y) is of the following form: y = mx + c, which is the equation of a line.

Continue reading Implementing ordinary least squares (OLS) using Statsmodels in Python

The open-source library react-native-gesture-handler is a great way to add gestures to cross-platform React Native apps. Two of the main reasons I find the library useful are because it uses native support to handle gestures, and it performs better on each native platform than React Native’s built-in touch system Gesture Responder system.

Continue reading How to Use the Pinch to Zoom Gesture in React Native Apps

As an exercise, or even to solve a relatively simple problem, many of you may have implemented linear regression with one feature and one target. However, in the real world, most machine learning problems require that you work with more than one feature.

For example, to calculate an individual’s home loan eligibility, we not only need his age but also his credit rating and other features.

Continue reading Implementing Multiple Linear Regression Using sklearn

Detecting objects, classifying them, and then keeping track of them is the biggest advancement in the deep learning area. There are many use cases of object detection in our day-to-day life; some examples are judging human behavior, activity trackers, some use in public sectors, crime detection, and many more.

In this article, I will help you deploy your own custom object detection based recommendation system to the web. In the end, you can interact with your real-time object detection and recommendation model using your web interface. Finally, you can host it over the cloud to access it everywhere, even with your mobile devices.

Continue reading Modeling & Deploying Object Detection Based Recommenders to the Web

With React Native, you can build cross-platform mobile applications using JavaScript as the primary programming language. Each of your mobile apps may contain single or multiple user interfaces to serve a wide range of purposes.

One such user interface is to upload images. In this tutorial, we’ll build a small demo app that allows the user to upload an image with associated details to Firebase’s real-time database Firestore and create a collection called posts. This collection of posts for the demo app will consist of an image and its title.

Continue reading Upload images in React Native apps using Firebase and Firestore

n this post, we will learn to develop a generative adversarial network (GAN) for generating realistic manga or anime characters.

I’ve always been amazed by vivid animations, especially Manga and their bold looks and strokes. Wouldn’t it be awesome to be able to draw a few ourselves, to experience the thrill of creating them with the help of a self-developed neural network?!

Continue reading My MangaGAN: Building My First Generative Adversarial Network

This tutorial discusses running the popular Python library NumPy (Numerical Python) in Android devices on top of the Kivy framework. The tutorial covers the Kivy installation, a brief introduction to Kivy, installation of python-4-android and Buildozer for building Android applications, and finally running NumPy in Android.

Python is one of the most popular data science tools due to the availability of powerful libraries that make building applications much easier. Some tasks, such as image classification, require writing many lines of code in programming languages such as C or Java, but Python decouples the user from many unnecessary details. It provides a simpler interface to do complex tasks. The simplicity extends to building machine learning models using a few lines of code. This makes Python a key tool for data scientists.

Continue reading Running NumPy in Android Devices using the Kivy Python Framework

🎯 The above paper was published in 2019 at the International Conference on Machine Learning (ICML). On the ImageNet challenge, with a 66M parameter calculation load, EfficientNet reached 84.4% accuracy and took its place among the state-of-the-art.

Continue reading Reviewing EfficientNet: Increasing the Accuracy and Robustness of CNNs